---
title: "Troubleshooting and FAQ"
metatags:
  description: "Troubleshooting and frequently asked questions for Ascend NPU"
---

Contributions are welcome. Feel free to add more.

## 1. Context corruption with GLOO op.preamble.length &lt;= op.nbytes in PD disaggregation

### Error message

```text highlight=2,5-7
[2026-04-07 13:24:13 TP0] Decode batch, #running-req: 10, #token: 485248, token usage: 0.94, pre-allocated usage: 0.51, #prealloc-req: 1, #transfer-req: 12, #retracted-req: 0, npu graph: True, gen throughput (token/s): 259.82, #queue-req: 0
[2026-04-07 13:24:13 TP0] Context corruption detected: Request 3b5dcfe1575d4e1f9b18c953de878a93 (bootstrap_room=7451500070298748792) received metadata from bootstrap_room=4125156593077881415. Metadata buffer index: 1. This indicates metadata buffer index collision.
[2026-04-07 13:24:13] INFO:      127.0.0.1:59272 - "POST /v1/chat/completions HTTP/1.1" 200 OK
[2026-04-07 13:24:13] INFO:      127.0.0.1:34000 - "POST /v1/chat/completions HTTP/1.1" 200 OK
terminate called after throwing an instance of 'gloo::EnforceNotMet'
 what():  [enforce fail at /pytorch/third_party/gloo/gloo/transport/tcp/pair.cc:456] op.preamble.length <= op.nbytes. 4 vs 3
Fatal Python error: Aborted

Thread 0x0000fff873f6f120 (most recent call first):
 File "/usr/local/python3.11.14/lib/python3.11/site-packages/sglang/srt/disaggregation/mooncake/conn.py", line 1499 in heartbeat_checker
```

### Cause

(Possibly, not precisely located) High-concurrency long sequences fill up the transfer buffer, causing buffer index collision and data corruption.

### Solution

1. Disable overlap on the Prefill node with `--disable-overlap-schedule`. The Prefill node in PD disaggregation must not enable overlap, otherwise it causes timing issues that lead to out-of-order reception on the Decode node.
2. Even with overlap disabled, multi-Prefill node high-concurrency long-sequence scenarios may still encounter this issue with low probability. This is a known issue pending resolution.

## 2. Graph mode aclnnInplaceFillScalar error

### Error message

```text highlight=2
(SGLangEngine pid=3872176) [rank0]:[E414 12:14:41.204711510 compiler_depend.ts:444] operator():build/CMakeFiles/torch_npu.dir/compiler_depend.ts:26 NPU function error:
 call aclnnInplaceFillScalar failed, error code is 507000
(SGLangEngine pid=3872176) [ERROR] 2026-04-14-12:14:41 (PID:3874122, Device:0, RankID:-1) ERR00100 PTA call acl api failed
(SGLangEngine pid=3872176) [Error]: An internal error occurs in the runtime module on the host.
(SGLangEngine pid=3872176) Rectify the fault based on the error information in the ascend log.
(SGLangEngine pid=3872176) [PID: 3874122] 2026-04-14-12:14:41.897.548 AclNN_Runtime_Error(EZ9903): aclrtLaunchKerneWithHostArgs failed, return: 507000
(SGLangEngine pid=3872176)  Solution: In this scenario, collect the plog when the fault occurs and locate the fault based on the plog.
(SGLangEngine pid=3872176)  TraceBack (most recent call last):
(SGLangEngine pid=3872176)  Check kernel task failed, stream_id=2028, task_id=48, retCode=0x7080005.[FUNC:LaunchKernel][FILE:context.cc][LINE:1585]
(SGLangEngine pid=3872176)  rtsLaunchKernelWithHostArgs execution failed, reason=kernel type error[FUNC:FuncErrorReason][FILE:error_message_manage.cc][LINE:61]
(SGLangEngine pid=3872176)  rtsLaunchKernelWithHostArgs failed, runtime result = 507000.[FUNC:ReportCallError][FILE:Log_inner.cpp][LINE:148]
(SGLangEngine pid=3872176)  aclrtLaunchKerneWWithHostArgs failed, return: 507000
(SGLangEngine pid=3872176)  Launch kernel failed.
(SGLangEngine pid=3872176)  #### KernelLaunch failed: /home/850b160/cann-8.5.8/opp/built-in/op_impl/ai_core/tbe//kernel/ascend910_93/ops_legacy/fill/Fill_41dadce325bOf810d03359af2a38990b_high_performance.o
(SGLangEngine pid=3872176)  Kernel Run failed. opType: 18, Fill
(SGLangEngine pid=3872176)  launch failed for Fill, errno:361001.
(SGLangEngine pid=3872176)
(SGLangEngine pid=3872176) Exception raised from operator() at build/CMakeFiles/torch_npu.dir/compiler depend.ts:26 (most recent call first):
(SGLangEngine pid=3872176) frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >)+ 0xb0 (0xffff806848c0 in /root/anaconda3/envs/slime_re/Lib/python3.11/site-packages/torch/lib/libc10.so)
(SGLangEngine pid=3872176) frame #1: c10::detail::torchCheckFail(char const*, char const*, unsigned int, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) + 0x68(0xffff8062c140 in /root/anaconda3/envs/slime_re/Tib/puthon3.11/site-packages/torch/lib/libc10.so)
(SGLangEngine pid=3872176) frame #2: <unknown function> + 0x110e2b4 (0xffff6d44e2b4 in /root/anaconda3/envs/slime_re/lib/python3.11/site-packages/torch_npu/lib/libtorch_npu.so)
(SGLangEngine pid=3872176) frame #3: <unknown function> + 0x29f0894(0xffff6ed30894 in /root/anaconda3/envs/slime_re/lib/python3.11/site-packages/torch_npu/lib/libtorch_npu.so)
(SGLangEngine pid=3872176) frame #4: <unknown function> + 0x9cc708(0xffff6cd0c700 in /root/anaconda3/envs/slime_re/lib/python3.11/site-packages/torch_npu/lib/libtorch_npu.so)
(SGLangEngine pid=3872176) frame #5: <unknown function> + 0x9cd2dc (0xffff6cd0d2dc in /root/anaconda3/envs/slime_re/lib/python3.11/site-packages/torch_npu/lib/libtorch_npu.so)
```

### Cause

Too many captured graphs cause a conflict in the graph mode update stream. Each graph is placed on a separate stream, but the number of streams is limited. If too many graphs are captured, conflicts occur.

### Solution

- CANN 8.5 + TorchNPU 2.8 should have resolved this issue.
- If your versions do not match, reduce the number of captured graphs to 10 or fewer.

## 3. alloc_extend_kernel error

### Error message

```text
[root@os-node-created-6z9tp pd_7p1d_tp2_20260414_030537]# grep -nR -E "aivec error|ACL" *,log
prefill 1.log:5739:EZ9999[PID: 164243] 2026-04-14-06:40:35.033.745 (EZ9999): The error from device(chipId:0, dieId:1), serial number is 1, there is an exception of aivec error, core id is 23, error code = 0, dump info: pc start: 0x12420156a000, current: 0x12420156ad74, vec error info: 0xdb1751f50e, mte error info: 0x98f6388707, ifu error info: 0x212c93fc00000, ccu error info: 0x998e981c00000000, cube error info: 0, biu error info: 0, aic error mask: 0x6500020bd00028c, para base: 0x12c100dccc00.[FUNC:PrintCoreInfo][FILE:device error core proc.cc][LINE:347]
prefill_1.log:5747:EZ9999[PID: 164242] 2026-04-14-06:40:35.034.456 (EZ9999):  The error from device(chipId:0, dieId:0), serial number is 1, there is an exception of aivec error, core id is 45, error code = O, dump info: pc start: 0x12400156a000, current: 0x12400156ad74, vec error info: 0x7304583407, mte error info: 0x27e2b1c765, ifu error info: 0x212c93fc00000, ccu error info: 0x6e0836f300000000, cube error info: 0, biu error info: 0O, aic error mask: 0x6500020bd00028c, para base: 0x12c100dccc0O.[FUNC:PrintCoreInfo][FILE:device_error_core_proc.cc][LINE:347]
prefill_1.Log:5784:RuntimeError: ACL stream synchronize failed, error code:507035
prefill_1.Log:5816:RuntimeError: ACL stream synchronize failed, error code:507035
prefill 2.log:2566:EZ9999[PID: 163475] 2026-04-14-05:28:43.282.016 (EZ9999):  The error from device(chipId:1, dieId:1), serial number is 1, there is an exception of aivec error, core id is 20, error code = 0, dump info: pc start: 0x124601550000, current: 0x124601550d74, vec error info: 0x9b07e496e3, mte error info: 0xc2a6806828, ifu error info: 0x212c93f200000, ccu error info: 0xaa64401100000000, cube error info: O, biu error info: 0, aic error mask: 0x6500020bd00028c, para base: 0x12c100c72400.[FUNC:PrintCoreInfo][FILE:device_error_core proc.cc][LINE:347]
prefill 2.log:2574:EZ9999[PID: 163474] 2026-04-14-05:28:43.282.809 (EZ9999):  The error from device(chipId:1, dieId:0), serial number is 1, there is an exception of aivec error, core id is 22, error code = 0, dump info: pc start: 0x124401550000, current: 0x124401550d74, vec error info: 0xa80496049a, mte error info: 0xa0770e2bf2, ifu error info: 0x212c93f200000, ccu error info: 0x1083000000000000, cube error info: O, biu error info: 0O, aic error mask: 0x6500020bd00028c, para base: 0x12c100c72400.[FUNC:PrintCoreInfo][FILE:device_error_core_proc.cc][LINE:347]
prefill 2.log:2611:RuntimeError: ACL stream synchronize failed, error code:507035
prefill 2.log:2644:RuntimeError: ACL stream synchronize failed, error code:507035
prefill 4.log:1323:EZ9999[PID: 163478] 2026-04-14-05:08:15.363.509 (EZ9999):  The error from device(chipId:3, dieId:0), serial number is 1, there is an exception of aivec error, core id is 5, error code = 0, dump info: pc start: 0x124c00dff000, current: 0x124c00dff860, vec error info: 0xf311fb8727, mte error info: 0x45418486a, ifu error info: 0x212c93fa00000, ccu error info: 0x17a9996f00000000, cube error info: 0, biu error info: 0, aic error mask: 0x6500020bd00028c, para base: 0x12c100d40c00.[FUNC:PrintCoreInfo][FILE:device_error_core_proc.cc][LINE:347]
prefill 4.log:1331:EZ9999[PID: 163479] 2026-04-14-05:08:15.364.016 (EZ9999):  The error from device(chipId:3, dieId:1), serial number is 1, there is an exception of aivec error, core id is 40, error code = 0, dump info: pc start: 0x124e00dff000, current: 0x124e00dff860, vec error info: 0xf11a9cf188, mte error info: 0xfb7710514a, ifu error info: 0x212c93fa00000, ccu error info: 0x1f5828a900000000, cube error info: 0, biu error info: O, aic error mask: 0x6500020bd00028c, para base: 0x12c100d40c00.[FUNC:PrintCoreInfo][FILE:device_error_core_proc.cc][LINE:347]
prefill 4.Log:1368:RuntimeError: ACL stream synchronize failed, error code:507035
prefill 4.log:1400:RuntimeError: ACL stream synchronize failed, error code:507035
prefill 7.log:3017:EZ9999[PID: 164628] 2026-04-14-05:34:12.369.755 (EZ9999): The error from device(chipId:6, dieId:0), serial number is 1, there is an exception of aivec error, core id is 33, error code = 0, dump info: pc start: 0x1258015ae000, current: 0x1258015aed74, vec error info: 0x740053222c, mte error info: 0x97103df5a0, ifu error info: 0x212c93f400000, ccu error info: 0x391c89ab00000000, cube error info: 0, biu error info: 0O, aic error mask: 0x6500020bd00028c, para base: 0x12c10Ocd6400.[FUNC:PrintCoreInfo][FILE:device_error_core_proc.cc][LINE:347]
prefill 7.log:3025:EZ9999[PID: 164629] 2026-04-14-05:34:12.369.742 (EZ9999):  The error from device(chipId:6, dieId:1), serial number is 1, there is an exception of aivec error, core id is 42, error code = 0, dump info: pc start: 0x125a015ae000, current: 0x125a015aed74, vec error info: 0xb91ae07b35, mte error info: 0xfc000670ef, ifu error info: 0x212c93f400000, ccu error info: 0x57c069b000000000, cube error info: 0, biu error info: O, aic error mask: 0x6500020bd00028c, para base: 0x12c10Ocd6400.[FUNC:PrintCoreInfo][FILE:device_error_core_proc.cc][LINE:347]
prefill 7.log:3062:RuntimeError: ACL stream synchronize failed, error code:507035
prefill 7.log:3094:RuntimeError: ACL stream synchronize failed, error code:507035
```

### Error plog

```text highlight=21
[ERROR] RUNTIME(164629,):2026-04-14-05:34:12.353.461 [stars engine.cc:1534]170327 ProcLogicCaReport:Task run failed, device id=13, stream id=43, task id=13145, sqe type=0(ffts), errType=0x1(task exception), sqSwStatus=0
[ERROR] RUNTIME(164629,):2026-04-14-05:34:12.369.720 device error core proc.cc:3211170327 AddExceptionReqInfo:add error register: core id=42, stream id=43, task id=13145
[ERROR] RUNTIME(164629,):2026-04-14-05:34:12.369.730 device error core proc.cc:3471170327 PrintCorelnfo:The error from device(chipld:6, dield:1), serial number is 1, there is an exception of aivec error, core id is 42, error code = O, dump info: pc start: 0x125a015ae000,current: 0x125a015aed74, vec error info: 0xb91ae07b35, mte error info: 0xfc000670ef, ifu error info: 0x212c93f400000, ccu error info: 0x57c069b000000000, cube error info: 0, biu error info: 0, aic error mask: 0x6500020bd00028c, para base: 0x12c100cd6400.
[ERROR] RUNTIME(164629,):2026-04-14-05:34:12.369.774 device error core proc.cc:3601170327 PrintCorelnto:The extend into: errcode:(0, 0x8000, 0) errorStr: When the D-cache reads and writes data to the UB, the response value returned bv the bus is a non-zero value. fixp_error0 info: 0x670ef, fixp error1 info: 0xfc, fsmId:0, tslot:2, thread:0, ctxid:0, blk:2, sublk:0, subErrType:4.
[ERROR] RUNTIME(164629,):2026-04-14-05:34:12.369.787 device error core proc.cc:4341170327 ProcessStarsCoreErrorInfo:devId=13, streamId=43, taskId=13145, MTE errorCode=0.
[ERROR] RUNTIME(164629,):2026-04-14-05:34:12.369.795 davinci task.cc:2011170327 SetStarsResultForDavinciTask:AIV Kernel happen error, retCode=0x31.
[ERROR] RUNTIME(164629,):2026-04-14-05:34:12.381.644 davinci kernel task.cc:15821170327 PreCheckTaskErr:Kernel task happen error retCode=0x31, vector core exception1.
[ERROR] RUNTIME(164629,):2026-04-14-05:34:12.381.705 davinci kernel task.cc:14241170327 GetArasInfo:[AIC INFO] aras(0 to 9) after execute:0x3fffffb9000, 0, 0, 0x12c9323ff600, 0x12c93231ee00, 0x12c93f1d8800,0x12c93f3ff600,0x12c958200000,0x100000003, 0xaaaa00000001.
[ERROR] RUNTIME(164629,):2026-04-14-05:34:12.381.710 [davinci kernel task.cc:1427]170327 GetArgsInfo:tilingKey = 0, print 1 Times totalLen=(10*8), argsSize=80, blockDim=3
[ERROR] RUNTIME(164629,):2026-04-14-05:34:12.381.717 [davinci kernel task.cc:1468]170327 PrintErrorInfoForDavinciTask:[AIC INFO] after execute:arqs print end
[ERROR] RUNTIME(164629,):2026-04-14-05:34:12.381.751 davinci kernel task.cc:14981170327 PrintErrorInfoForDavinciTask:[DFX INFO]Aicore kernel execute failed, device id=13, stream id=43, report stream id=43, task id=13145, flip num=56, fault kernel_name=alloc_extend _kernel_18, fault kernel info ext=alloc_extend_kernel, program id=141, hash=14069671779787989248.
[ERROR] IDEDD(164629,):2026-04-14-05:34:12.381.823 [dump manager.cpp:41][tid:170327] An exception callback message is received.
[ERROR] IDEDD(164629,):2026-04-14-05:34:12.381.971 [kernel info collector.cpp:384][tid:170327] Get error register information. coreNum=0
[ERROR] IDEDD(164629,):2026-04-14-05:34:12.381.981 kernel info collector.cpp:4771tid:1703271 It is Non-SuperKernel. functionCount=1, qlobalCount=1
[ERROR] IDEDD(164629,):2026-04-14-05:34:12.381.987 [dump args.cpp:668][tid:170327] In arqAddr[0x12c100cd6400]|arqSize[80]dfxAddr[(nil)]|dfxSize[0] has invalid attribute.
[ERROR] IDEDD(164629,):2026-04-14-05:34:12.383.894 [dump_printf.cpp:1118][tid:170327] infoAddr is null
[ERROR] RUNTIME(164629,):2026-04-14-05:34:12.383.908 [stream.cc:1332]170327 GetError:Stream Synchronize failed, stream id=43, retCode=0x31, [vector core exception].
[ERROR] RUNTIME(164629,):2026-04-14-05:34:12.383.911 [stream.cc:1335]170327 GetError:AIV Kernel happen error, retCode=0x31.
[ERROR] RUNTIME(164629,):2026-04-14-05:34:12.383.929 [stream.cc:1335]170327 GetError:[AIC_INFO] after execute:args print end
[ERROR] RUNTIME(164629,):2026-04-14-05:34:12.383.936 stream.cc:13351170327 GetError: DFX INFO1Aicore kernel execute failed, device id=13, stream id=43, report stream id=43, task id=13145, tlip num=56,
 fault kernel_name=alloc_extend_kernel_18, ault kernel info ext=alloc_extend_kernel, program id=141, hash=14069671779787989248.
[ERROR] RUNTIME(164629,):2026-04-14-05:34:12.383.943 [stream.cc:3549]170327 EnterFailureAbort:stream id=43 enter failure abort.
[ERROR] RUNTIME(164629,):2026-04-14-05:34:12.383.973 [stars_engine.cc:1427]170327 StarsResumeRtsa:stop scheduling in abort failure mode: stream id=43, sq id=6,sq head=801, task id=13145, taskType=66.
[ERROR] RUNTIME(164629,):2026-04-14-05:34:12.383.972 [stream.cc:1463]164629 SynchronizeExecutedTask:context is abort, status=0x715005e.
[ERROR] RUNTIME(164629,):2026-04-14-05:34:12.383.978 [stream.cc:1516]164629 Synchronizelmpl:failed, stream_id=43, error=0x715005e
[ERROR] RUNTIME(164629,):2026-04-14-05:34:12.383.982 [api error.cc:1015]164629 StreamSynchronize:Stream synchronize failed, stream_id=43, timeout=-1ms.
[ERROR] RUNTIME(164629,):2026-04-14-05:34:12.383.991 [apic stream.cc:154]164629 rtStreamSynchronize:ErrCode=507035, desc=[vector core exception], InnerCode=0x715005e
[ERROR] RUNTIME(164629,):2026-04-14-05:34:12.383.997 [error_message_manage.cc:61]164629 FuncErrorReason:rtStreamSynchronize execution failed, reason=vector core exception
[ERROR] ASCENDCL(164629,):2026-04-14-05:34:12.384.269 [stream.cpp:140]164629 acIrtSynchronizeStreamImpl:synchronize stream failed, runtime result = 507035
```

### Cause

The `alloc_extend_kernel` operator appears to have a memory allocation issue. Pending resolution.

### Solution

Modify `sglang/srt/hardware_backend/npu/allocator_npu.py` to comment out the affected branch and use the else branch instead.

```python highlight=15-36
    def alloc_extend(
        self,
        prefix_lens: torch.Tensor,
        prefix_lens_cpu: torch.Tensor,
        seq_lens: torch.Tensor,
        seq_lens_cpu: torch.Tensor,
        last_loc: torch.Tensor,
        extend_num_tokens: int,
        num_new_pages: int = None,
    ):
        ...
        if num_new_pages_item > len(self.free_pages):
            return None

        # if num_new_pages_item < 200:
        #     from sgl_kernel_npu.mem_cache.allocator import alloc_extend_kernel

        #     out_indices = torch.empty(
        #         (extend_num_tokens,),
        #         dtype=torch.int64,
        #         device=self.device,
        #     )
        #     max_num_extend_tokens = next_power_of_2(extend_num_tokens)
        #     bs = prefix_lens.shape[0]
        #     alloc_extend_kernel[(bs,)](
        #         prefix_lens,
        #         seq_lens,
        #         last_loc,
        #         self.free_pages,
        #         out_indices,
        #         next_power_of_2(bs),
        #         self.page_size,
        #         max_num_extend_tokens,
        #     )

        # else:
        out_indices = torch.empty(
            (extend_num_tokens,),
            dtype=torch.int32,
            device=self.device,
        )
        ...
```

## 4. Out of NPU memory

```text highlight=7
  File "/home/code/sglang/python/sglang/srt/model_executor/pool_configurator.py", line 175, in calculate_pool_sizes
    return MemoryPoolConfig(max_total_num_tokens=max_total_num_tokens)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "<string>", line 8, in __init__
  File "/home/code/sglang/python/sglang/srt/model_executor/pool_configurator.py", line 44, in __post_init__
    raise RuntimeError(msg)
RuntimeError: Not enough memory. Please try to increase --mem-fraction-static.
```

### Solution

First, use the `npu-smi info` command to check the NPU memory usage.

If the NPUs are occupied by other processes, use `--base-gpu-id` to specify the starting device index.

If the NPUs are not occupied, you can use `--tp` to deploy across multiple devices, or reduce the KV cache memory usage by increasing the `--mem-fraction-static` value. For detailed tuning guidance, see [Hyperparameter Tuning](/docs/advanced_features/hyperparameter_tuning).

## 5. How to update sgl-kernel-npu

### Solution

```bash
git clone https://github.com/sgl-project/sgl-kernel-npu.git

source /usr/local/Ascend/ascend-toolkit/set_env.sh
cd sgl-kernel-npu
# Building Project
bash build.sh

pip install output/sgl_kernel_npu*.whl --force-reinstall

# (Optional) Confirm whether the import can be successfully
python -c "import sgl_kernel_npu; print(sgl_kernel_npu.__path__)"

rm -rf sgl-kernel-npu
```

## 6. `[Errno 101] Network is unreachable` when downloading HuggingFace datasets

### Error message

```text highlight=1-2
'[Errno 101] Network is unreachable' thrown while requesting HEAD https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
Retrying in 1s [Retry 1/5].
Traceback (most recent call last):
  File "<frozen runpy>", line 198, in _run_module_as_main
  File "<frozen runpy>", line 88, in _run_code
  File "/usr/local/python3.11.14/lib/python3.11/site-packages/sglang/bench_serving.py", line 2353, in <module>
    run_benchmark(args)
  File "/usr/local/python3.11.14/lib/python3.11/site-packages/sglang/bench_serving.py", line 1848, in run_benchmark
    input_requests = get_dataset(args, tokenizer, model_id)
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/python3.11.14/lib/python3.11/site-packages/sglang/benchmark/datasets/__init__.py", line 44, in get_dataset
    return dataset.load(tokenizer=tokenizer, model_id=model_id)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/python3.11.14/lib/python3.11/site-packages/sglang/benchmark/datasets/random.py", line 45, in load
    return sample_random_requests(
           ^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/python3.11.14/lib/python3.11/site-packages/sglang/benchmark/datasets/random.py", line 89, in sample_random_requests
    dataset_path = download_and_cache_hf_file(
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/python3.11.14/lib/python3.11/site-packages/sglang/benchmark/utils.py", line 98, in download_and_cache_hf_file
    return hf_hub_download(repo_id=repo_id, filename=filename, repo_type=repo_type)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  ...
RuntimeError: Cannot send a request, as the client has been closed.
[ERROR] 2026-05-18-11:58:31 (PID:215, Device:-1, RankID:-1) ERR99999 UNKNOWN application exception
```

### Cause

The machine cannot directly access the HuggingFace server due to network restrictions (firewall, proxy, or regional access limitations).

### Solution

- **Use an HF mirror site** — set the `HF_ENDPOINT` environment variable to a mirror (e.g., hf-mirror.com):

  ```bash
  export HF_ENDPOINT=https://hf-mirror.com
  ```

- **Use a proxy** — if you have an HTTP proxy available:

  ```bash
  export http_proxy=http://your-proxy:port
  export https_proxy=http://your-proxy:port
  ```

- **Download the dataset manually** — use a machine with network access to download the file, then transfer it to the target machine. Use `--dataset-path` to specify the local file path:

  ```bash
  python -m sglang.bench_serving \
    --dataset-name random \
    --dataset-path /path/to/ShareGPT_V3_unfiltered_cleaned_split.json \
    ...
  ```

## 7. Unexpected type fp8

### Cause

FP8 model is not supported

### Solution

Replace model weights, e.g., switch Qwen/Qwen3.5-27B-FP8 to Eco-Tech/Qwen3.5-27B-w8a8-mtp.

## 8. Docker image versions: stable release vs. daily build

Docker images for Ascend NPU are available in two types:

- **Stable release** — validated version with a specific tag, e.g., `quay.io/ascend/sglang:cann9.0.0-a3-v0.5.16`.
  Recommended for production deployments.
- **Daily build** — includes the latest development changes, e.g., `quay.io/ascend/sglang:main-cann8.5.0-a3`. Use this
  if you need the latest features or bug fixes that have not yet been included in a stable release.

If you encounter issues with a stable release, try switching to a daily build to see if the issue has been resolved in the latest development version.
